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DOE on track to deliver high-burnup SNF to Idaho by 2027
The Department of Energy said it anticipated delivering a research cask of high-burnup spent nuclear fuel from Dominion Energy’s North Anna nuclear power plant in Virginia to Idaho National Laboratory by fall 2027. The planned shipment is part of the High Burnup Dry Storage Research Project being conducted by the DOE with the Electric Power Research Institute.
As preparations continue, the DOE said it is working closely with federal agencies as well as tribal and state governments along potential transportation routes to ensure safety, transparency, and readiness every step of the way.
Watch the DOE’s latest video outlining the project here.
Madicken Munk, Rachel N. Slaybaugh
Nuclear Science and Engineering | Volume 193 | Number 10 | October 2019 | Pages 1055-1089
Technical Paper | doi.org/10.1080/00295639.2019.1586273
Articles are hosted by Taylor and Francis Online.
Methods for deep-penetration radiation transport remain important for radiation shielding, nonproliferation, nuclear threat reduction, and medical applications. As these applications become more ubiquitous, the need for accurate and reliable transport methods appropriate for these systems persists. For such systems, hybrid methods often obtain reliable answers in the shortest time by leveraging the speed and uniform uncertainty distribution of a deterministic solution to bias Monte Carlo transport and reduce the variance in the solution. This work reviews the state of the art among such hybrid methods. First, we summarize variance reduction (VR) for Monte Carlo radiation transport and existing efforts to automate these techniques. Relations among VR, importance, and the adjoint solution of the neutron transport equation are then discussed. Based on this exposition, the work transitions from theory to a critical review of existing VR implementations in modern nuclear engineering software. At present, the Consistent Adjoint-Driven Importance Sampling (CADIS) and Forward-Weighted Consistent Adjoint-Driven Importance Sampling (FW-CADIS) hybrid methods are the gold standard by which to reduce the variance in problems that have deeply penetrating radiation. The CADIS and FW-CADIS methods use an adjoint scalar flux to generate VR parameters for Monte Carlo radiation transport. Additionally, efforts to incorporate angular information into VR methods for Monte Carlo are summarized. Finally, we assess various implementations of these methods and the degree to which they improve VR for their target applications.